A Connectionist Approach to Speech Understanding

This paper argues that connectionist systems are a good approach to implement a speech understanding computational model. In this direction, we propose SUM, a speech understanding model, which is a software architecture based on neurocognitive researches. The SUM´s computational implementation applies wavelets transforms to speech signal processing and connectionist models to syntactic parsing and prosodic-semantic mapping. This approach enables a system of analysis by compensation, when the syntactic analysis does not offer a good reliable level, it is possible to evaluate prosodic-semantic analysis, such as in human speech understanding.